Mechanisms for Late 20th and Early 21st Century Decadal AMOC Variability
Bibliographic record
Abstract
Where earlier generations of ocean models with resolution of 1° or coarser tended to represent wintertime dense water formation in the North Atlantic mainly as a process of open water convection in the Labrador Sea and Nordic Seas, more recent models with higher resolution, in conjunction with observational programmes such as OSNAP, have presented us with a new, more complex, picture. Watermasses are progressively ventilated and lose buoyancy as they propagate cyclonically westward around the gyre, starting with the formation of Subpolar Mode Water close to the eastern boundary, and eventually leading to Labrador Sea Water, which forms part of the lower limb of the Atlantic meridional overturning circulation (AMOC). We present a set of hindcast integrations of a global 1/4° NEMO ocean configuration from 1958 until nearly the present day, forced with three standard surface forcing datasets. We use the surface-forced streamfunction, estimated from surface buoyancy fluxes, along with the overturning streamfunction, similarly defined in potential density space, to investigate the causal link between surface forcing and decadal variability in the strength of the AMOC. We confirm that surface heat loss from the Irminger Sea is the dominant mechanism for decadal AMOC variability, while that from the Labrador Sea has about half the amplitude. The AMOC variability is shown to be related to that of the North Atlantic Oscillation, primarily through the surface heat flux, itself dominated by the air-sea temperature difference, and we show that a metric based on the surface-forced streamfunction has predictive value for AMOC variability on interannual to decadal time scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".